This variation was attributed to the PEO blocks ofF-68 extended to the solution phase, whereas the PPO blocks of F-68functioned as anchors for adsorption onto the photoresist. Therefore,the F-68-adsorbed layer prevented interaction between the photoresistparticles or the photoresist and the ITO surface, which provides potentialfor future applications as new stripping agents with high removalperformance.Photoresist strippingis the final step in the photolithographyprocess that forms fine patterns for electronic devices. Recently,a mixture of ethylene carbonate (EC) and propylene carbonate (PC)has attracted attention as a new stripper based on its eco-friendlinessand anti-corrosiveness. However, the EC/PC mixture causes re-adsorptionof the photoresist during a process of subsequent water rinsing. Inthis study, we characterized the adsorption/desorption of the photoresistand a triblock Pluronic surfactant [poly(ethylene oxide)–poly(propyleneoxide)–poly(ethylene oxide)] as a blocking agent on an indiumtin oxide (ITO) substrate. In addition, we evaluated the dispersionof photoresist particles. The photoresist polymer formed a thin andrigid adsorption layer on an ITO substrate in the EC/PC mixture. Whenwater was injected into the EC/PC mixture and the photoresist solutions,the photoresist polymer aggregated and was then deposited on the substrate.In contrast, the addition of Pluronic surfactant F-68 (PEO Amine-type agents, such as monoethanolamine and N-methylpyrrolidone, are often used as strippers for photoresistfilms; however, these agents may damage the metal surface.6 Hence, alternatives to these reagents are required to avoid potentialdamage to the metal surface.Photoresists are photofunctional materialsused in the electronicsindustry. Photoresist films are mainly composed of polymers and photosensitizersand possess both etching resistance and light-induced developer solubility.These films are widely known as chemical agents for forming fine metalpatterns in applications such as liquid crystal displays, semiconductordevices, microelectromechanical systems, and integrated circuits.8 These compounds have been considered new photoresist-stripping agentsbecause they are less toxic and corrosive to the substrate than amine-typeagents.6 However, the photoresist stripped from thesubstrate can be readily redeposited onto the surface via water rinsing.Amphiphilic materials are typically added to prevent such unexpectedadsorption.Alkylene carbonates, particularlyethylene carbonate (EC) and propylenecarbonate (PC), are extensively used in many industrial applications.9 This techniqueprovides information on the adsorption and desorption of target materialsin water or organic solvents through sensor coating. Hence, QCM-Dhas been widely employed as a tool for monitoring dynamic interactionsbetween solid and liquid interfaces. One of these applications isthe non-specific adsorption of proteins or cell adhesion on surfaces.Hydrophobic surfaces or stainless steel covered with Pluronic surfactants or PEO homopolymers reportedlyimpede the adsorption of proteins.11 In the presenceof such polymer brushes on the surfaces, the stretched polymer chainsbecome entropically unfavorable and cause steric repulsion betweenthe chains and proteins. The effect of the PEO-based coating on therepel adsorption is related to the grafting density and chain length.The quartz crystal microbalance with dissipationmonitoring (QCM-D)technique is highly sensitive to the real-time mass change and viscoelasticproperties of adsorbed films.12 The surfactants were dissolved in the EC/PC mixture, withthe PEO and PPO chains as solvophilic and solvophobic groups, respectively;the longest PEO chain analogue  formed a polymer brush on the silica surface. Furthermore,we demonstrated the dispersion state of photoresist particles andtheir adsorption/desorption behavior on an indium tin oxide (ITO)substrate in an EC/PC/water mixture with and without F-68.13 QCM-D revealed that the F-68 adsorption layerprevented aggregation between the photoresist particles and theiradsorption on the ITO substrate. However, the conformation of theF-68 molecules adsorbed on the photoresist is still unclear. Particularly,the conformation or adsorption layer structure in the region of water-richcompositions is crucially important, according to the enhanced anti-adsorptionability of F-68 in such a region.13 Inaddition, there is a lack of discussion about the adsorption kineticsin organic solvents, including our EC/PC mixtures.In our previous studies, we characterized the adsorption of Pluronicsurfactants with different PEO chain lengths on silica in a mixtureof EC and PC using atomic force microscopy (AFM).In this study,we monitored the adsorption and desorption processesof the photoresist on an ITO substrate in an EC/PC mixture with F-68by QCM-D. The adsorption structure of F-68 on the photoresist in eithera water/EC/PC mixture or pure water was analyzed by small angle X-rayscattering (SAXS) and AFM. Our findings on the systematic evaluationof anti-adsorption can serve as an important platform for many industries,including not only electronics but also textiles, metals, foods, andbiomaterials.12EC and PC used were obtained from Kanto Chemical without further purification. The weight ratio of theEC/PC mixture was fixed at 70/30.79PPO30PEO79)] was obtained from ADEKA  and used withoutpurification. The average molar masses of the PEO and PPO chains are6600 and 1750 g mol–1, respectively; the total averagemolar mass is 8350 g mol–1. The concentration ofF-68 was set to 1 or 10% w/w.The Pluronic surfactant  and n is the overtone number. Theenergy dissipation shift ΔDn is obtained simultaneously with the frequency shift and isdefined according to Elost isthe dissipated energy and Estored is thetotal energy stored in the oscillator during an oscillation cycle.The liquid flow rate was maintained at 0.1 mL/min, and the selectedovertone numbers (n) were 5, 7, and 9. All the experimentswere conducted at 25 °C.QCM-D measurements were performedusing a Biolin Scientific QSense Explorer instrument. The relationshipbetween the shift in frequency (Δequation 1.151wher16 which enables the estimationof film thickness (d), shear elastic modulus (μ),and shear viscosity (η) by fitting the experimental data forΔFn (Dn (l/(ρl·ω))1/2), and 2πf, respectively; the subscripts Q, l, and j representthe quartz crystal, liquid medium, and adsorbed film, respectively. N is the number of the viscoelastic overlayers (assumedto be 1). Both viscoelastic parameters, μ and η, can berelated to the complex elastic modulus (G*) through 17G′ and G″ arethe storage and loss modulus of the adsorbedfilm, respectively; the ratio of G″/G′ allowed quantification of the viscoelastic propertiesof the film.For thick and soft films, theQCM-D profiles exhibited a high energy dissipation shift and overtonenumber (frequency) dependence. In this study, we applied the widelyused Voigt model, forΔFn 3 and ΔDnΔFn  using a quick freezer  and cut with a glassknife. The replica was prepared by exposing the cross-section of thefrozen dispersion to platinum vapor, followed by treatment with carbonvapor to build the replica using a freeze-replica apparatus . The replica was washed with a chloroform/methanol(v/v = 2/1) solution, acetone, and water. The replica was visualizedat an acceleration voltage of 100 kV.q < 2 nm–1). Background subtraction was performed on the 1D data. These conversionsand background subtractions were calculated using the XSACT software.All measurements were conducted at room temperature.SAXS measurements were performedusing a Xenocs Xeuss 3.0 instrument to evaluate the structure of thephotoresist dispersion. The sample was sealed in a quartz capillarywith an inner diameter of 1.5 mm and irradiated with Cu Kα X-ray(wavelength of 0.154 nm) from a camera length of 1000 mm for 10 min.Scattered X-rays were collected using a detector . The beam had a diameter of 400 μm (high-resolutionmode). The obtained two-dimensional scattering pattern was circularlyaveraged and converted into one-dimensional (1D) data (scattered vectorrange of 0.01 < 19 The intensity can be describedaccording to I(q) is the scattering intensity, n is the number densityof the particles, P(q) is the formfactor providing information on the particle size and shape, and S(q) is the structure factor representingthe particle interaction potential. In this study, the coherent scatteringbetween particles was negligible [S(q) = 1]20 because of the diluted photoresistsystems (0.1% w/w), whereas the intensity was proportional to P(q) (p(r) is the pair-distance distribution function (PDDF)and r is the distance between two scattering centerswithin the particle.The PDDF is a histogram of the distance within the scattering particles,converging to a zero level beyond the maximum diameter within theparticle. As the PDDF also depends on the electron density insidethe particle, the theoretical curve reflects the particle size, shape,and internal structure. Furthermore, the electron density profile[Δρ(r)] was calculated from the PDDFusing the deconvolution method. Thus, Δρ(r) yields information on the size of the domains at a distance (r) from the center of the particle.The scatteringprofiles were further analyzed using the indirect Fourier transformation(IFT) method. to P(q) 7.7where 12 The force curves were obtained in an F-68 aqueoussolution (1% w/w) and then measured similarly in water after repeatedreplacement with pure water 10 times. All experiments were conducted10 min after the immersion of the ITO sensor at room temperature.AFManalysis was performed using a HitachiHigh-Tech AFM 5200S/5200II instrument to evaluate the adsorption structureof F-68 on the photoresist film in water. We used triangular siliconnitride cantilevers ; the cleaning of thecantilevers was carried out according to a previous study.Before examining the prevention of the re-adsorptionof the photoresistfor the Pluronic surfactant, the adsorption and desorption behaviorswithout additives must be understood. The QCM-D results obtained inthe absence of Pluronic F-68 are shown in Fn/n and ΔDn decreased and increased,respectively. These shifts reflect the variation in bulk viscosityand density between liquids (the bulk effect) rather than the adsorptioneffect. In practice, the shifts were nearly identical to the valuescalculated using F5/5 = −273 Hz, ΔF7/7 = −231Hz, and ΔF9/9 = −204 Hz;ΔD5 = 110 × 10–6, ΔD7 = 93.3 × 10–6, and ΔD9 = 82.3 × 10–6), assuming that the parameter for the adsorbed film(subscript j) was 0. For the above calculations,the viscosity (ηl) and density (ρl) of the EC/PC mixture were experimentallydetermined to be 2.42 × 10–3 kg/(m·s)and 1322 kg/m3, respectively. The discrepancy between thecalculated and experimental values for ΔFn/n may result from the ITOsurface roughness. Upon injecting the photoresist solution, the ΔFn/n and ΔDn changed further, indicatingthat a photoresist adsorption layer was formed on the ITO substrate.Even after rinsing with water, the shifts did not reach the initialbaseline levels. Then, we confirmed monodispersed (primary-like) andaggregated (secondary-like) particles on the ITO sensor surface afterQCM-D measurements . The adsorptionof 1–10 μm sized particles reportedly causes a positivefrequency shift occasionally in the QCM-D measurement,26 whereas, in this case, the ΔFn/n showed negativeshifts at all overtone numbers, which suggests that the photoresistparticles adsorbed strongly and firmly onto the ITO substrate.When water was replaced with theEC/PC mixture, Δn = 5, 7, and 9), we analyzed the viscoelastic propertiesof the photoresist film in an EC/PC mixture based on the Voigt model.21 We then calculated the offset from the baselinelevels in the EC/PC mixture, resulting in a photoresist film thicknessof 3.3 nm, a shear elastic modulus of 12 × 105 kg/(m·s2), a shear viscosity of 11 × 10–3 kg/(m·s),and a G″/G′ ratioof 0.28. The photoresist film density (ρj) was assumed to be 1250 kg/m3.22 Considering that the film is thin and G″/G′ is less than 1, the photoresiststrongly interacts with the ITO substrate and forms a rigid and elasticfilm. The shear elastic modulus of the photoresist film on the ITOsubstrate in the EC/PC solvent was significantly higher than thatof high-molecular-weight materials  in previous studies.25From the frequency and dissipation shifts in several overtone numbers(Fn/n decreased by approximately 200 Hz (t = 9.0 min in t ≤ 9.2 min), and finally, a gradual decrease (9.2 ≤ t min). This behavior simultaneously reflects the bulk effectand desorption of the photoresist film. With water rinsing, the ΔFn/n increasedto approximately −28 Hz for all overtones, whereas ΔDn almost reached the baselinelevels, indicating that the photoresist remained rigid on the ITOsurface. Even after the same removal procedure, the QCM-D profileswere similar, and the photoresist could not be removed using onlythe EC/PC mixture.We investigated the removability of the residual photoresistbythe EC/PC mixture. Fn/n and ΔDn profiles were similar for all the overtone numbers. Therefore,the QCM-D results are described for n = 5 in thefollowing discussion.The QCM-D results measured in the presence of PluronicF-68 are shown in F5/5 decreasedto −35 Hz, whereas ΔD5 increasedto 20 × 10–6, suggesting the formation of anF-68 soft layer on the ITO substrate. In this measurement, the presenceof the layer observed in the AFM profiles was also confirmed, as QCM-Dcould contain the bulk effect . Although the results may include bulk effects, Pluronicsurfactants have been reported to form brush structures with anchoredPPO chains27 and mushroom structures.28 Remarkably, both ΔF5/5 and ΔD5 decreased afterthe injection of the F-68/photoresist solution; the ΔD5 shift reflects the detachment of F-68 fromthe ITO and/or the shrinking of the F-68 polymer chains. After rinsingwith water, both ΔF5/5 and ΔD5 reached near-baseline levels.On replacing the F-68 and EC/PC mixture with theEC/PC mixtureon the ITO substrate, Δ2 without F-68 and1.53 mg/m2 with F-68; that is, the anti-adsorption effectwas higher than 95%. In addition, the F-68 system exhibited good reproducibility.The residual amount was 4.78 mg/m2 after repeated rinsingwith EC/PC solvent and water  the anti-adsorption kinetics of the photoresistby the F-68 adsorption layer and (ii) the interaction between thephotoresist and F-68 via water rinsing. These investigations havethe potential to provide insights into the mechanism of the characteristicanti-adsorption.F is the normalizedadsorption ratio, Ft isthe frequency shift after a time t, Finitial is the frequency shift at 0 s, and Fmax is the frequency shift at 10 min. In the absence ofF-68, the adsorption ratio exceeded 50% in less than 30 s and reached90% within 80 s, whereas the addition of F-68 prevented the photoresistadsorption rate. This indicates that the adsorption of the photoresistpolymers is suppressed by the PEO chain of F-68 extending from theITO surface to the bulk solution.We examined the adsorption kinetics of thephotoresist without F-68 1a and wi29 thus preventing photoresist aggregation by F-68 adsorption on thephotoresist particles.We analyzed the structure of F-68 adsorbed on the photoresistbymeans of FF-TEM and SAXS in a mixture of water/EC/PC 5 and 6 a–1 for photoresist aggregateswithout F-68. When F-68 was added, the band shifted to a wider anglethan in the absence of F-68. We assumed that the band was caused bythe complex structure formed between the photoresist particle andF-68 because the scattering data for the F-68 solution did not detectany characteristic intensity.SAXS measurements were performed to investigate the structureofthe photoresist dispersions. p(r) represents the total value of the product of electrondensity fluctuations in a minute volume at both ends of an arbitrarydistance, r, for all possible combinations of twopoints in the particle, and the curve provides information on theparticle size and shape.20 The highestdiameter of the photoresist particle without and with F-68 was calculatedas 73.0 and 40.5 nm, estimated from the zero-convergence point, respectively;these sizes were correlated with the FF-TEM images. As expected, thecurves were distinguishable depending on the presence or absence ofF-68. Without F-68, the PDDF curve can be described as a bell-shape,30 suggesting a homogeneous electron density fluctuationinside the particle regardless of whether it is positive or negative.Conversely, the classic core-corona structure31 was formed for the curve in the presence of F-68. Hence, the complexof the photoresist and F-68 would have a hydrophobic photoresist asthe core, and the PEO and PPO chains of F-68 as the corona and anchorparts, respectively. According to a previous study,32 F-68 forms micelles at 40% w/w or more at room temperature.In particular, although F-68 was dissolved as unimers, it possiblyformed a core-corona complex with the photoresist in the EC/PC/watermixture. This hypothesis is also supported by the PDDF profile ofthe pure P-123 micelle system,20 whichshows a bell-like shape owing to the similarities in the structuresof PEO and PPO. Therefore, the electron density of the photoresistis suggested to be lower than that of PEO and PPO.r = 0 and converges at approximately40 nm, corresponding to the radius. Δρ(r) is positive because of the uniform internal electron density. Incontrast, the electron density profile of the photoresist was lowerthan that of the solvent, and the negative electron density profiledemonstrated the hydrophobic core of the complexes with F-68. As thedistance from the center of the sphere increases, the profile passesthrough the zero point, which is defined as the length of the hydrophobiccore. Subsequently, Δρ(r) becomes positiveand converges to zero, which is determined as the length of the hydrophiliccorona. The obtained thicknesses of the hydrophilic corona and thehydrophobic core were 11.0 and 11.8 nm, respectively. Consideringthat the thickness of the hydrophilic corona (11.0 nm) is shorterthan the contour length of the PEO chain (28 nm),12 the PEO chains are expected to be folded within the corona.Despite the negative Δρ(r) of the photoresistin the complex with F-68, the photoresist particle without F-68 showedconstantly positive value. This is because the PDDF curve showed positive p(r) even though the Δρ(r) was homogeneously negative inside the photoresist basedon the definition of the p(r).33 The repulsive interactions remained even after repeated washingwith water. In the absence of F-68, an attractive force was detectedbetween the photoresist and the cantilever tip in water .AFM analysis was performed to evaluate the interaction forces betweenthe F-68 adsorption layers. Approaching force curves were obtainedin an F-68 aqueous solution (1% w/w) and water after 10 cycles ofwater replacement 7. In bot34 Milner etal. describe the density distribution function of a polymer brushas a parabolic profile based on the self-consistent field theory.This model can be applied to the force curve using the Derjaguin approximation.36 We also performed symmetric measurements (both surfaces coveredwith F-68) and asymmetric measurements (one photoresist surface coveredwith F-68), corresponding to the theoretical formulas in 36 This theory enables theestimation of the uncompressed brush thickness (L) and the average distance between grafting points (s) by fitting the experimental data.L0 is theequilibrium brush thickness (L0 is assumedto be 1.3L19), D is the surface separation distance, R is the radius of the cantilever tip , kB is the Boltzmann’s constant, T is the absolute temperature, N is thenumber of segments in the polymer chain, and a isthe segment length. We assigned the symmetric and asymmetric modelsto the F-68 aqueous and water-rinsing systems, respectively. Thisassumption is based on the QCM-D results, in which the F-68 on silicawas almost completely desorbed by rinsing with water .Fora discussion of the film properties, we analyzed the compressionforce curve data using the Milner–Witten–Cates (MWC)theory.L) was 19.8 (in F-68 solution) and17.3 nm (after rinsing with water), respectively. This similaritysuggests that the PPO chain of F-68 was strongly adsorbed on the photoresistfilm. The brush thickness obtained from AFM (in water) was largerthan the hydrophilic corona thickness (11.0 nm) calculated from theSAXS deconvolution profile. This variation was affected by the presenceof the EC/PC mixture in the SAXS measurements. Generally, the polymerbrush extends into the bulk solution because of its higher affinityfor the solvent and larger adsorption amount.37 Considering that the EC/PC mixture is more solvophilic than water,12 the shorter thickness estimated by the SAXSmeasurements was probably caused by a decreased adsorption amountof F-68 in the presence of EC/PC in the solvent. However, the resolutionand sensitivity of AFM and SAXS must be considered. The other fittedparameter, s, in the F-68 solution and water wasestimated to be 6.3 and 2.8 nm, respectively. The dense packing afterwater rinsing may be attributed to the loose packing of F-68 adsorbedon the cantilever tip, resulting in the loss of resistance to compression.In this study, we characterized the adsorption/desorptionbehaviorsof Pluronic F-68 and a photoresist on/from an ITO substrate to understandthe anti-adsorption mechanism during the photoresist-stripping process.In addition, we investigated the dispersion of photoresist particles.The results were compared with and without F-68. QCM-D measurementsrevealed that the photoresist adsorbed rigidly on ITO in a mixed solutionof EC and PC, and the amount of adsorbed photoresist further increasedupon water rinsing. This residue could not be removed from the surfaceby repeated rinsing with EC/PC. In contrast, the adsorption amountof the photoresist decreased remarkably in the presence of F-68. Thiseffect can be attributed to two factors. The first factor is the physicalinhibition of the photoresist on the ITO substrate. Compared to bareITO, the surface covered with F-68 featured the effect of impedingthe adsorption rate of the photoresist. The second factor is the contributionof the photoresist particles to the dispersion stability. Hydrophobicphotoresist polymers aggregated on the substrate and in the bulk solutionwhen rinsed with water. However, SAXS and AFM measurements suggestthat F-68 forms a core-corona structure with photoresist particlesin the bulk and a brush structure on the photoresist film. These structures,in which the PEO chains extend into the bulk and the PPO chains areanchored to the photoresist, prevent aggregation between photoresistparticles and are consistent with the FF-TEM images. Consequently,we proposed a mechanism to prevent photoresist adsorption with F-68,which could become a platform for many cleaning technologies as wellas in the electronics industry.


Our study aimed to investigate the associations of glucose tolerance status with insulin-like growth factor-I (IGF-I) and other clinical laboratory parameters of acromegalic patients before and after the patients underwent transsphenoidal adenomectomy (TSA) by conducting a single-center, retrospective study.A total of 218 patients with acromegaly who had undergone TSA as the first treatment were retrospectively analyzed. Serum IGF-I, growth hormone (GH) and glucose levels were measured before and after surgery.The follow-up levels for random GH, GH nadir, and the percentage of the upper limit of normal IGF-I (%ULN IGF-I) were decreased significantly. The percentages of normal (39.0%), early carbohydrate metabolism disorders (33.0%) and diabetes mellitus (28.0%) changed to 70.2%, 16.5% and 13.3%, respectively, after TSA. %ULN IGF-I at baseline was higher in the diabetes mellitus (DM) group than in the normal glucose tolerance group and impaired glucose tolerance (IGT) /impaired fasting glucose (IFG) groups before TSA, and the DM group exhibited a greater reduction in %ULN IGF-I value after surgery. The follow-up %ULN IGF-I value after surgery was significantly lower in the improved group, and Pearson's correlation analysis revealed that the reductions in %ULN IGF-I corresponded with the reductions in glucose level.This study examined the largest reported sample with complete preoperative and follow-up data. The results suggest that the age- and sex-adjusted IGF-I level, which reflects altered glucose metabolism, and the change of it are associated with improved glucose tolerance in acromegalic patients both before and after TSA. Acromegaly results from the persistent hypersecretion of growth hormone (GH) and insulin-like growth factor-I (IGF-I), which causes most of the clinical manifestations of acromegaly. The etiology of more than 95% of acromegaly cases is a GH-secreting pituitary adenoma . UncontrAlthough several studies have investigated the relationship between IGF-I level and insulin resistance, few studies have investigated the correlations between IGF-I or other clinical laboratory parameters and glucose tolerance status, and none have reported a positive correlation between IGF-I and glucose tolerance status both before and after transsphenoidal adenomectomy (TSA) in a large sample with complete follow-up data –10. TherIn this study, we analyzed 345 consecutive patients who had been diagnosed with acromegaly caused by a GH-secreting pituitary adenoma without dysfunctions of other endocrine axes and who had undergone TSA at Peking Union Medical College Hospital  between July 1, 2012 and December 31, 2014. Data regarding routine medical procedures were collected retrospectively. All 345 patients underwent TSA via the same procedure, and the diagnosis of each patient was confirmed by experienced pathologists who analyzed the tumor tissue after surgery.n = 218) stratified by preoperative glucose tolerance status, i.e., normal glucose tolerance (NGT) (n = 85), IGT/IFG (n = 72), and diabetes mellitus (DM) (n = 61), are shown in 2, 124.06 ± 16.53 mmHg, and 77.06 ± 12.35 mmHg. Women composed 58.7% of the subjects. The follow-up interval of the patients was defined as the time interval between surgery and the first follow-up date, and the mean follow-up interval was 127.23 ± 71.04 days.Among the 345 patients, 74 received medical treatment for acromegaly and 21 received gamma-knife radiosurgery before surgery. A total of 250 patients received TSA as the first treatment. The tumors in 5 of these patients were not completely removed during surgery, and 27 patients were lost to follow-up. The remaining 218 patients who underwent surgery that was performed by surgeons with equivalent surgical experience were selected for data analysis. The baseline characteristics of the patients  and an elevated serum IGF-I level. Pituitary adenoma was confirmed by magnetic resonance imaging (MRI) and by clinical features, including acral enlargement, increased skin thickness, increased sweating, DM, hypertension, headache, sleep apnea and osteopenia. Glucose tolerance status was evaluated by the 75-g OGTT. According to WHO criteria , NGT wasAll of the serum samples were collected early in the morning after an eight-hour fasting period. The serum basal GH concentration was defined as the fasting GH concentration prior to administration of the 75-g OGTT. GH nadir (GHn) values were obtained during the 75-g OGTT, which was performed after a 12h fast. Blood samples were drawn to assess the baseline GH, IGF-I and glucose levels, with GH and glucose being assessed at 30, 60, 120 and 180 min. The follow-up 75-g OGTT was performed 127.23 ± 71.04 days after surgery, at the first follow-up. It is strongly recommended that IGF-I assays be calibrated using the WHO international standard of highly purified recombinant IGF-I (WHO IS 02/254). Furthermore, the GH assay should be calibrated to the 22-kDA isoform standard or, as a second choice, to multiple isoforms .The common consensus criteria are an IGF-I level in the age-adjusted normal range and a GH level < 1.0 μg/L from a random GH measurement, with nadir GH levels < 0.4 μg/L in patients undergoing neurosurgery .2 test for categorical data and the t-test or ANOVA for continuous data. Associations between the two clinical laboratory parameters were assessed by Pearson's correlation coefficient analysis. A two-tailed p < 0.05 was considered to indicate statistically significant differences.All statistical analyses were performed using IBM SPSS 20.0 software . The data are reported as the mean ± SD for normally distributed continuous variables and as the number and percentage for dichotomous variables. The IGF-I level was calculated and expressed as a percentage of the upper limit of the normal IGF-I (%ULN IGF-I) level for each subject according to age using the Peking Union Medical College Hospital laboratory samples (%ULN=(IGF-I-ULN)/ULN×100%). Data were compared between groups using the χp = 0.000) and IGT/IFG (p = 0.000) groups, but it did not differ between the NGT and IGT/IFG groups (p = 0.871). The SBP was higher in the DM group than in the NGT group (p = 0.011).Our study showed that 28.0% of the patients had diabetes and 33.0% had IFG or IGT, with the remaining 39.0% of patients having normal glucose tolerance. The mean follow-up interval was 127 ± 71 days, with no significant difference between groups. There were gradual increases in age and SBP from NGT to IGT/IFG to DM. The patient age was higher in the DM group than in the NGT (p = 0.005); this level was higher in the DM group than in the NGT (p = 0.001) and IGT/IFG (p = 0.028) groups, but it did not differ between the NGT and IGT groups (p = 0.325). The GH and GHn levels did not differ significantly among the groups.The preoperative clinical laboratory parameters are shown in p = 0.000) and NGT groups (p = 0.000), but there was no significant difference between the IGT/IFG and NGT groups (p = 0.145). The change from the baseline Glu2h was significantly different among the three groups (p = 0.000). The Glu2h of the DM group decreased the most, followed by the IGT/IFG group, and the differences between any two of the three groups were significant (p = 0.000).After TSA, the average Glu0h  and Glu2h (2-h glucose level on OGTT) decreased significantly in the IGT/IFG and DM groups . The chap < 0.01). The changes from the baseline GH and GHn levels did not differ significantly among the groups. However, the change from the baseline %ULN IGF-I of the DM group was higher than were those of the IGT/IFG (p = 0.004) and NGT groups (p = 0.000), whereas it did not significantly differ (p = 0.191) between the IGT/IFG and NGT groups. According to the remission criteria, 53.3% (116 of 218) of the patients achieved a random GH level of < 1.0 μg/L, 56.9% (124 of 218) achieved a GHn level of < 0.4 μg/L, 33.5% (73 of 218) achieved a normal age-adjusted IGF-I level, and 30.7% (67 of 218) achieved both a GHn level of < 0.4 μg/L and a normal age-adjusted IGF-I level. We compared the percentage of patients with a random GH level of < 1.0 μg/L, GHn level of < 0.4 μg/L, and normal age-adjusted IGF-I level among the three groups after surgery. However, no significant differences were observed.As shown in p = 0.020, OR = 1.006) and change from the baseline %ULN IGF-I  were the variables most significantly correlated with preoperative glucose status for these patients.Logistic regression analysis including age, BMI, SBP, follow-up Glu0h, follow-up Glu2h, and the parameters random GH, GHn and %ULN IGF-I was carried out to identify parameters associated with preoperative glucose status. The baseline %ULN IGF-I (n = 44), including the patients with IGT/IFG before surgery who experienced restoration of NGT after surgery and those with DM before surgery but with IGT/IFG or NGT after surgery; a stable group (n = 87), including the patients with persistent IGT/IFG or DM before and after surgery; and a deteriorated group (n = 10), including the patients with IGT/IFG before surgery who developed DM after surgery. Because the number of patients in the deteriorated group was too low to generate accurate statistics, this group of patients was not included in the comparative analysis. The glucose status changes in the three groups after surgery are shown in To determine how the clinical parameters are related to preoperative glucose tolerance status after surgery, the patients with abnormal glucose tolerance (IGT/IFG and DM) were divided into the following three groups according to the preoperative and postoperative glucose tolerance status : an imprp = 0.021), whereas the average age in the improved group was significantly higher than that in the stable group (p = 0.013).As shown in p < 0.01). The change from the baseline Glu2h in the improved group was significantly higher than that in the stable group (p = 0.001); the mean change from the baseline Glu0h was also higher in the improved group, but this difference was not statistically significant (p = 0.074).The baseline and follow-up Glu0h and Glu2h of the improved group were significantly lower than those of the stable group (p = 0.001), whereas the baseline and change from the baseline %ULN IGF-I did not significantly differ. Moreover, the improved group had a significantly higher proportion of patients with a random GH level of < 1.0 μg/L (p = 0.031) and normal age-adjusted IGF-I level (p = 0.032) than the stable group. In addition, the improved group had an increased proportion of patients with a GHn level of < 0.4 μg/L (p = 0.068) that approached statistical significance.The baseline, follow-up, and change from the baseline levels of random GH and GHn were similar in both groups. However, the follow-up %ULN IGF-I after surgery was significantly lower in the improved group than in the stable group  and could be predicted by younger age . According to the ROC curve analysis, the age cut-off value for predicting an improvement in glucose tolerance status after TSA was 45.5. The sensitivity and specificity were 59.1% and 29.9%, respectively, and the AUC was 63.3% (53.4%-73.2%) (p = 0.013).Logistic regression analysis of age, BMI, and the parameters random GH, GHn, and %ULN IGF-I showed that an improvement in glucose tolerance status was associated with a lower follow-up %ULN IGF-I (n = 73) and discordant (n = 145) groups according to the IGF-I criterion of a cure  to compare the glucose levels between the two groups , and the average follow-up Glu2h was also lower in the cure group, approaching significance (p = 0.070).The data showed that the follow-up Glu0h of the cure group was significantly lower than that of the discordant group (p < 0.01). Reductions in the %ULN IGF-I corresponded with reductions in the glucose level . The changes in the %ULN IGF-I before and after surgery were correlated positively with changes in the glucose level . The GH-related indexes were not significantly correlated with any of the glucose parameters.We analyzed the relationships among the %ULN IGF-I and GH levels and glucose parameters. The %ULN IGF-I before surgery was positively correlated with all parameters before surgery, including the GH, GHn, Glu0 and Glu120 levels  and peripheral insulin resistance in adipose tissue and muscles ,16. In cWe also found significant decreases in the levels of biochemical parameters, including Glu0h, Glu2h, random GH, GHn, and %ULN IGF-I, in our patients after surgery. These results suggest that TSA can indeed improve the glucose status in acromegaly, which should be analyzed in a large single-center sample. In accordance with previous observations ,22,23, w2 test, the proportions of biochemically cured patients did not significantly differ among the three groups, but they were slightly higher in the DM group than in the other two groups. In addition, we found that the patients with preoperative DM had significantly greater decreases in Glu0h, Glu2h, and %ULN IGF-I after surgery than in the IGT/IFG and NGT groups (According to the χT groups . These rT groups , and stuT groups . TherefoT groups . One posT groups . SimilarWe analyzed the clinical parameters associated with the improved glucose tolerance status in this sample. The glucose tolerance of the IGT/IFG group was more likely to be improved than that of the DM group. In accordance with this expectation, the improved group had better glucose tolerance and more easily achieved a biochemical cure than the stable group, especially with regard to the random GH and normal age-adjusted IGF-I levels. The results also revealed that the patients in the improved group were younger, that fewer were female, and that they had a lower %ULN IGF-I at follow-up. As age is one of the risk factors for decreased glucose tolerance in acromegaly , youngerSimilar to previous studies, the IGF-I levels fluctuated during the early postoperative period in this study . A limitIn our study, a lower follow-up %ULN IGF-I was associated with an improved glucose status at an average of 133.76 days after surgery, indicating that the IGF-I level at approximately 4 months after surgery was already predictive of the glucose status. These findings indicate the importance of the early measurement of IGF-I in patients with an abnormal blood glucose level. These results were further confirmed by comparisons of the glucose levels according to the IGF-I criterion of a cure . The folFurthermore, to confirm the correlation between the %ULN IGF-I and glucose levels before and after surgery, Pearson's correlation analysis was performed. The correlations detected between the laboratory indexes indicate that the change in %ULN IGF-I between before and after surgery might be predictive of an altered glucose level, despite the weak correlation observed. However, no significant relationships were found between the differences in the random GH or GHn levels before versus after surgery and the glucose parameters. Thus, the IGF-I level was a better indicator than the GH-related indexes of altered glucose metabolism in the acromegalic patients.This conclusion is in agreement with those of previous studies reporting a closer correlation of IGF-I with indexes of insulin resistance than nadir or random GH –10. HopkThe specific physiological and pathological mechanisms underlying the correlation of IGF-I with glucose status in acromegaly is still unclear. However, the measurement of serum IGF-I may be the best single test for the diagnosis of acromegaly and may reflect the activity of the disease, as serum IGF-I concentrations do not vary according to daily activities but instead reflect integrated GH secretion during the preceding day or longer ,34. ThisThese observations suggest that the age- and sex-adjusted IGF-I level before and after TSA and its change are associated with improved glucose tolerance in acromegalic patients both before and after TSA. We suggest that the continuous monitoring of the serum IGF-I level is necessary to evaluate blood glucose improvement in these patients. For better long-term prognosis, improved control of blood glucose should be one of the goals in the management of acromegaly. Considering this goal, we advise placing more emphasis on the IGF-I level at 3 months after surgery. A lower level indicates better recovery and control of DM and the IGF level; thus, more accurate criteria for the cure and control of acromegaly as well as guidelines that consider the treatment of complications must be established.There are several limitations in our study. Our study was designed as a retrospective study with a relatively short follow-up period; thus, a long-term association of IGF-I with glucose metabolism could not be examined. Moreover, with incomplete data, we failed to assess other markers for glucose metabolism before and after surgery, such as the HbA1c level, serum insulin levels and insulin sensitivity. As ours was a single-center study, additional multi-center studies should be performed.In conclusion our study of acromegalic patients is the first to report complete preoperative and follow-up data. The results suggest that the age- and sex-adjusted IGF-I level is an effective parameter that reflects changes in glucose metabolism and that is associated with improved glucose tolerance in acromegalic patients both before and after TSA. Careful monitoring of the serum IGF-I level is recommended to evaluate blood glucose improvement in acromegalic patients.


This RFI-suppressed SMOS TB dataset, spanning more than a decade (since 2010), is expected to provide opportunities for better retrieval of land parameters and scientific applications.The Soil Moisture Ocean Salinity (SMOS) was the first mission providing L-band multi-angular brightness temperature (TB) at the global scale. However, radio frequency interferences (RFI) and aliasing effects degrade, when present SMOS TBs, and thus affect the retrieval of land parameters. To alleviate this, a refined SMOS multi-angular TB dataset was generated based on a two-step regression approach. This approach smooths the TBs and reconstructs data at the incidence angle with large TB uncertainties. Compared with Centre Aval de Traitement des Données SMOS (CATDS) TB product, this dataset shows a better relationship with the Soil Moisture Active Passive (SMAP) TB and enhanced correlation with  The SMOS satellite was launched on November 2, 2009, to a low-earth, polar sun-synchronous orbit with a mean altitude of 758-km, and flight forward with a fixed forward titled angle of 32.5° between the boresight and the local nadir. The SMOS satellite is equipped with a synthetical aperture radiometer, which is a Y-shaped structure with 69 antenna elements regularly distributed (0.875 wavelengths)9. This configuration ensures the unique capability of measuring multi-angular (about −10° to 60°) TB with 43-km average ground resolution over land (from 27-km at the nadir and limited to 55-km on the edges). Each pair of the small antennas measure the correlation of microwave radiation  for a given spatial frequency that depends on the relative position of the two antennas. With the correlations measured by all antenna pairs it is possible to form an image through a kind of inverse Fourier transform. The width of the resulting snapshot image at each integration step is about 1200 km, then the globe can be fully imaged twice every 3 days at 6:00 A.M. (ascending) and 6:00 P.M. (descending) local solar time at the equator.The data from European Space Agency’s (ESA) and Centre National d’Etudes Spatiales (CNES) SMOS satellite are widely used in a variety of applications over both land and oceans. Applications over land include the water cycle . In addition, the image reconstruction noise and aliases on the edges induce noisier TB12, in particular for low incidence angles that are observed in the region of the field of view where the sky alias is corrected . Like other satellites, SMOS suffers from RFI contamination in certain areas of the world. Being the first L-band radiometer, problems were not expected in the protected band, so no filtering approaches were implemented in the receivers. This leads to data loss or meaningless retrievals of SMOS TBs14. Great success was obtained in reducing RFI problems since the SMOS launch, but many RFI sources still need to be located, filtered, or canceled, and new RFI sources also appear every day15. Therefore, apart from the flagging approaches developed at the Level 1 and 2 products in the SMOS processors, different kinds of methods have been proposed to post-process SMOS multi-angular TB. For example, a fixed width binned average has been used in the CATDS Level-3 (L3) daily SMOS multi-angular TB product, which also transforms the observed TB polarizations to a ground-based reference frame16. Some studies fitted multi-angular SMOS TBs with an exponential function17, a quadratic function18, or a third-order polynomial function19. Zhao et al. proposed a sophisticated method, named two-step regression, to refine the multi-angular SMOS TBs20. The approach was proposed based on a large number of simulations with a wide range of natural conditions using radiative transfer models. The two-step regression approach has been shown to perform best compared to other methods21 and has been used in the retrieval of soil moisture, snow density, and sea ice25.To achieve the scientific objective of observing sea surface salinity over oceans and soil moisture over land with high accuracy, the SMOS satellite is well-calibratedThe current research aims to produce an RFI-suppressed SMOS multi-angular TB dataset using the two-step regression approach. This dataset is intended to provide improved opportunities for better scientific applications over land.26 from 2010 to 2021 (version 724) was refined using the two-step regression approach. The full polarized TB observations at the antenna frame are provided to 15-km in an equal-area discrete global grid (DGG) system  in the SCLF1C product. The flowchart of the post-processor is shown in Fig. The ESA SMOS Level-1C Full Polarization (SCLF1C) product27. As there is no complete dataset for any epoch, a simple first-order spline interpolation was employed to fill the missing acquisitions. The anomaly filtering criteria comprise three steps: (1) TB thresholds filtering; (2) Binned quantile filter using 25% (Q1) and 75% quantile (Q3) with inter-quartile range (IQR), employing bins with a width of 5°, covering central angles ranging from 2.5° to 67.5°; (3) A time series anomaly detection using mean and standard deviation with a moving window size of 10 snapshots. Same as the latest CATDS L3 daily TB product (version 330)28, the RFI and the sun correction flag were not utilized in the procedure.Firstly, TB observations at the antenna frame were filtered with various criteria  were converted to horizontal (H-) or vertical (V-) polarized TBs at the ground reference frame with the following equation:20. The detailed filtering criteria at the ground reference are also listed in Table Thirdly, a further filter procedure was applied to address potential outliers introduced during the interpolation procedure. For example, TB at H-polarization should be lower than TB at vertical polarization at non-nadir incidence theoretically20. The first step used a quadratic function to obtain the TB at the nadir angle based on the finding from simulations that the total intensity of the electromagnetic wave is changed smoothly with the incidence angle. The quadratic function is shown in below:The final procedure involves the implementation of the two-step regression approach proposed by Zhaoin which, θ is incidence angle, A and C are parameters fitted by non-linear Levenberg-Marquardt (LM) algorithm. C is the first Stokes parameter in the nadir angle, and half of C is used as a control point in the second step of the regression to avoid the polarization mixing, as it represents the TB value for both H- and V-polarization at the nadir angle. Another objective of Eq.  is to ina, b and d are parameters fitted by LM algorithm, the subscript H and V represent H- and V-polarization respectively. Specific bounds were set for these parameters to ensure the desired trends of the curves. For example, parameter b denotes changes in the magnitude and direction of TB. It is set to be greater than 1 for V-polarization (bV) and less than 1 for H-polarization (bH) in the regression; dV represents the Brewster angle effect in V-polarization with a lower bound of 1.The second step aims to obtain the multi-angular characteristics of H- and V-polarized TB using the following function separately:entclass1pt{minimaFollowing the above procedure, the refined TBs at any incidence angle ranging from 0° to 65° can be calculated with reduced RFI and aliasing effects. The interpolation allows for smooth estimation of refined TBs even at angles that were not directly observed in a snapshot.To enable effective filtering and comprehensive evaluation of the two-step regression approach’s result and the impact of RFI according to different application requirements, various statistical metrics were computed at different steps, as illustrated in the yellow rhomboids in Fig. (1) TBs used in applying the two-step regression approach.(2) TBs directly interpolated at XY and converted to HV without any filtering using the procedure “TB XY interpolation directly” and the subsequent “TB XY to HV”.For each comparison, both the overall and binned bias and root mean square deviation (RMSD) between the respective TBs and the fitted TB at corresponding incidence angles were calculated. The binned statistics were derived with a 5° width, covering central angles ranging from 2.5° to 62.5°, including an incidence angle of 40°.For convenient application and better comparison with CATDS SMOS L3 daily TB and SMAP Level-2 TB in this study, the above-refined TBs and some auxiliary variables in the product files were resampled to a global Equal Area Scalable Earth Grid version 2 (EASE-GRID 2.0) 25-km and 36-km with a Hamming window approach using a footprint of 43-km.28, generated from Level-1B snapshot products and projected to global EASE-GRID 2.0 25-km. The product provides fixed-angle binned TB with the above-mentioned fixed-width binned average method after lots of flag filtering and quality assessment16. The CATDS multi-angular products are in operation (OPER) and reprocess (RE07) modes. The RE07 mode product covers the period from 12 January 2010 to 24 May 2021, and the product in OPER mode ranges from 25 May 2021 till now.To validate the refined multi-angular TB, they were compared with the TB from the CATDS L3 daily TB product29. TB from SMAP L2 Radiometer Half-Orbit 36-km EASE-GRID Soil Moisture  with composite release ID of R1829031 was also used to compare with refined SMOS TB at 40°. SMAP TB products are projected to global EASE-GRID 2.0 36-km. To be noted, SPL2SMP uses the TB inherited from SMAP L1C Radiometer Half-Orbit 36-km EASE-GRID TBs . As the SCLF1C TB contains water bodies’ effect in the DGGs, the TB before adjustment for the presence of water bodies was extracted for comparison.SMAP is less affected by RFI because of RFI detection techniques applied to the hardware and software32, including the ISEA4H9 grid and the EASE-GRID 2.0 25-km and 36-km grid system. All files are stored in the netCDF4 format. For the ISEA4H9 gridded data, it includes:basic observation information, such as geolocations, DGG time, counts of strong or other types of RFI, and aliasing from the SCLF1C flag.refined SMOS multi-angular TBs and corresponding flag range from 2.5° to 62.5°, with an interval of 5°, including TB and flag at the incidence angle of 40°.abundant auxiliary statistics at the incidence angles of refined TB. These statistics comprise the overall and binned standard deviation of the original and filtered TBs, the bias, and the RMSD between the original, filtered TB, and the refined TB, as indicated in the yellow rhomboids in Fig. 33, Akaike and Bayesian information criterion34 statistics.regression results, providing all parameters in Eqs. , 3, degrThe refined SMOS multi-angular TBs dataset is located at National Tibetan Plateau Data Center (TPDC) and is provided in three types of projectionsAdditionally, the dataset in the EASE-GRID 2.0 projection contains variables that can be resampled to 25-km and 36-km grids, along with corresponding uncertainty. However, variables such as counts in (1) and regression results in (4) that cannot be resampled are not included in the dataset. To be noted, the resample method may introduce uncertainties in the results, and the standard deviation of a variable for resampling in an EASE-GRID 2.0 cell was used to evaluate the interpolation artifacts. More details can be found in the user guide.32 can be accessed at 10.11888/Terre.tpdc.300406 or https://cstr.cn/18406.11.Terre.tpdc.300406. Detailed description of the data records32 can be found in the readme.pdf file and codes and procedures for processing and plotting the figures are also included.The ISEA4H9 gridded filename was inherited from the SMOS SCLF1C orbit file with ascending or descending flag added in the filename. The EASE-GRID 2.0 grided files have a suffix about the resample method and grid size. All the datasetsBelow analyses are presented to support the technical quality of the refined SMOS multi-angular TB dataset.24. And CATDS TB exhibits more fluctuations than the refined TBs because CATDS TB was designed to keep characteristics of the real observations as much as possible, and the two-step regression was proposed to refine the TB for remote-sensed parameter retrieval. Despite these differences, all three types of TBs have the same multi-angular trend, which indirectly represents that the Hamming function resample method is reliable.Figure 35. These variations can impact the refined TB results. When TB jumps occur at several incidence angles, the two-step regression approach would smooth out these jumps or fail to achieve a robust refinement. If TB changes occur across the entire range of observations, the refined results may be shifted accordingly.The multi-angular observation capability of SMOS also presents challenges for the two-step regression approach when observing areas with high heterogeneity . In the land-ocean interface, there can be abrupt TB jumps between different incidence angles due to variations in surface properties. When low and intermediate incidence angles observe the land, large incidence angles predominantly capture ocean regions, leading to sudden decreases in TB. Similarly, in mountainous areas, the topographic effect can cause distinct changes in TB behaviour, e.g., the V-polarized TB tends to decrease, while the H-polarized TB increasesFigure 36 was used to filter the CATDS L3 TBs. The filtering criterion is expressed as:36. No RFI filtering in CATDS L3 TB would have greater available counts, but the unfiltered TBs may be less reliable, as indicated in Fig. The spatiotemporal coverage of TBs is another important aspect of evaluating the RFI-suppression performance of refined TB. The available TB counts at incidence angles of 27.5°, 40° and 52.5° were calculated and presented in Fig. 37.Figure in-situ soil moisture from 19 dense networks (Table θ and P polarization. Ts is the soil temperature extracted from the CATDS auxiliary data (https://data.catds.fr/cpdc/Common_products/GRIDDED/AUX/), projected in EASE GRID 2.0 25-km.Further, it is necessary to demonstrate the potential advantages of using this refined SMOS TB for soil moisture retrieval. The total emissivity was calculated using the refined SMOS TBs and compared with ks Table  at the s38. Additionally, the application of the multi-channel collaborative algorithm (MCCA)39 to multi-frequency observations, such as those from the Advanced Microwave Scanning Radiometer (AMSR)40, has revealed a notable trend in VOD: VOD values increase with frequency for each land cover type. This finding provides valuable insights into the characteristics of VOD across different frequencies and land cover conditions. This dataset may be promising to analyse the polarized characteristics of VOD using mono-polarization-multi-angular observations based on the MCCA.For each soil moisture observation network, a bounding rectangle was defined using the maximum and minimum longitude and latitude of network stations. The emissivity of the EASE-GRID 2.0 cells located within this rectangle was averaged and compared with the average soil moisture for validation. To be noted, the Pearson’s correlation coefficients (R) were calculated based on the same available counts, and a weak RFI filtering criterion, as described in Eq. , was use41 were used as evaluation metrics. Figure 42. The second row of Fig. To illustrate the performance of the refined SMOS TB dataset, TB at 40° (resampled to EASE-GRID 2.0 36-km) was compared with the SMAP TB product. To assess the correlation and differences between the two datasets, R, bias (unit: K), RMSD (unit: K), and unbiased root mean square deviation 44. For Fig. To further illustrate the RFI-suppressed performance of the refined TB, SMAP TB product was used to compare with the refined and CATDS TB at 40°. Because the SMOS TBs were resampled to EASE-GRID 2.0 36-km, it is impractical to resample the flag in the CATDS TB product, a less strict RFI filtering criterion as described in Eq. , the CAT45. The stability of TBs is particularly high all over Antarctica (Fig. 46. The mean values of Dome-C’s H- and V-polarized TBs were 187.059 K and 211.509 K, respectively, with standard deviations of 1.033 K and 1.143 K. In the current study, the selected calibration DGGs are not located in Dome-C. As shown in Table 38.To evaluate the stability of SMOS instruments and explore the possibility to have an operative long-term calibration target for subsequent L-band satellites, a simple demonstration was conducted with the refined TB in 2021, and the results are shown in Fig. ica Fig. . The Dom32 spanning over a decade. The two-step regression method proposed by Zhao et al.20 was used to refine or interpolate the multi-angular SMOS TB affected by field aliasing and RFI. The resulting refined TBs in DGGs were then resampled to the EASE-GRID 2.0 projection, with two grid sizes of 25 km and 36 km, using a Hamming window approach with a footprint of 43 km.In this study, we have presented an RFI-suppressed SMOS multi-angular TB dataset32 has demonstrated great RFI-suppression performance. It exhibits strong agreement with SMAP TB at 40°, and the corresponding emissivity derived from the refined TBs shows a better relationship with in-situ soil moisture compared to the CATDS TB in most cases. This dataset32 can be used to retrieve essential land parameters such as soil moisture and snow density etc. Moreover, it is expected to provide opportunities for advancing scientific applications over land.As described in the previous section, this refined SMOS TB dataset


Human Respiratory Syncytial Virus (RSV) is one of the leading causes of lower respiratory tract infections (LRTI), responsible for infecting people from all age groups—a majority of which comprises infants and children. Primarily, severe RSV infections are accountable for multitudes of deaths worldwide, predominantly of children, every year. Despite several efforts to develop a vaccine against RSV as a potential countermeasure, there has been no approved or licensed vaccine available yet, to control the RSV infection effectively. Therefore, through the utilization of immunoinformatics tools, a computational approach was taken in this study, to design a multi-epitope polyvalent vaccine against two major antigenic subtypes of RSV, RSV-A and RSV-B. Potential predictions of the T-cell and B-cell epitopes were followed by extensive tests of antigenicity, allergenicity, toxicity, conservancy, homology to human proteome, transmembrane topology, and cytokine-inducing ability. The peptide vaccine was modeled, refined, and validated. Molecular docking analysis with specific Toll-like receptors (TLRs) revealed excellent interactions with suitable global binding energies. Additionally, molecular dynamics (MD) simulation ensured the stability of the docking interactions between the vaccine and TLRs. Mechanistic approaches to imitate and predict the potential immune response generated by the administration of vaccines were determined through immune simulations. Subsequent mass production of the vaccine peptide was evaluated; however, there remains a necessity for further in vitro and in vivo experiments to validate its efficacy against RSV infections. Paramyxoviridae, is known to be the primary cause of lower respiratory tract infections (LRTI), including pneumonia and bronchitis—in infants, children, as well as elderly and immunocompromised individuals2. RSV is an enveloped virus that contains a single-stranded, negative-sense RNA with a genome size of about 15.2 kb. As of yet, two major RSV antigenic subtypes have been identified, RSV-A and RSV-B, exhibiting differential sequence divergence throughout their genome; RSV-A has been seen to be more prevalent than RSV-B3. However, the two subtypes can coexist and thrive, owing to RSV reinfections being common throughout the life of an infected individual, indicating that cross-immunity against distinct strains is only partial4. RSV severely affects immunocompromised infants as well as the geriatric population with weakened immune systems. The implicated virus infection is considered globally to be the second largest cause of death, in children under one year of age. RSV-associated acute LRTI is responsible for around 33 million serious respiratory infections globally every year, according to the World Health Organization (WHO); resulting in more than 3 million hospitalizations and about 60,000 deaths of children under 5 years of age, and 6.7% of all deaths in infants younger than a year-old. RSV infects the cells, lining the human respiratory pathway, including the ciliated epithelial cells, and causes upper and lower respiratory tract complications. Influenza-like diseases and LRTI display clinical symptoms of serious RSV infection5. Consequently, over the past two decades, RSV has become a major focus for vaccination studies to decrease the morbidity of lower respiratory tract infections. Despite the failure of the first RSV vaccine trial in 1960, many vaccines have been developed and investigated, but no one is licensed yet for commercial use7. In addition to vaccine development approaches, the use of RSV anti-infective drugs as a palliative treatment option is being explored as a potential strategy to fight against RSV infections8. Merely two approved RSV antivirals are currently available, which include, palivizumab, a humanized preventive monoclonal antibody, and aerosolized ribavirin for therapy. While these two antivirals can help alleviate the symptoms of RSV infections, they are not ideal for prophylactic use. Furthermore, the efficacy of ribavirin in improving outcomes for severe RSV patients remains uncertain10. Hence, the available research highlights the urgent need to develop effective RSV vaccines to successfully combat the infections caused by the virus.The Human Respiratory Syncytial Virus (hRSV), a member of the family of 11. Whereas, N protein surrounds the viral genome of RSV, and the P protein is a vital component of the viral RNA-dependent RNA polymerase complex which is necessary for the proper replication and transcription of RSV12. Hence, this study aimed to identify potential epitopes from the P, N, F, and mG proteins of RSV, with the goal of designing a polyvalent vaccine capable of stimulating an immune response to combat RSV infection.In this study, an immunoinformatics approach was used to generate a polyvalent epitope-based vaccine blueprint capable of inducing a substantial immune response against both RSV-A and RSV-B types. We have targeted four highly virulent proteins for designing the subunit vaccine such as phosphoprotein (P protein), nucleoprotein (N protein), fusion glycoprotein (F protein), and major surface glycoprotein (mG protein). The F protein mediates the fusion and attachment of the virus to its target cells along with the mG protein, thus facilitating viral entryFrom the NCBI database, the RSV viral strains and the query proteins were identified. Following that, the four RSV-A and RSV-B Query Proteins including P protein, N protein, F protein, and mG, were retrieved from the UniProt online database. The UniProt Accession Number and the length of the query proteins are listed in Table All of the selected proteins were found to be antigenic in the initial antigenicity analysis and the biophysical property analyses of the query proteins are listed in Potential epitopes of P protein, N protein, F protein and mG protein are listed in The population coverage analysis showed that 85.70% and 87.92% of the world population were covered by the MHC class-I and class-II alleles and their epitopes, respectively, and 84.62% of the world population was covered by the combined MHC class-I and class-II. While India had the highest percentage of population coverage for the CTL epitopes (87.56%) as well as HTL epitopes 93.51%), China had the highest percentage of population coverage for CTL and HTL epitopes in combination (91.80%) , indicating the vaccine to be quite stable. The extinction coefficient and the aliphatic index of the vaccine were also found to be high with values, 45,770 M−1 cm−1 and 80.85, respectively.The vaccine sequence was constructed by linking the most promising T-cell and B-cell epitopes with appropriate linkers. Additionally, hBD-3 adjuvant and PADRE sequence were conjugated in the appropriate positions. A schematic presentation of the vaccine sequence is illustrated in Fig. The secondary structure of the vaccine protein was analyzed, revealing that the coil structure had the largest number of amino acids, while the β-strand showed the lowest percentage. Predictions from four different servers, as depicted in Fig. A 3D model of the vaccine was generated using the RaptorX online server, which had a significantly good quality with a low p-value of 8.71e-05 in four domains. Homology modeling was performed using 1KJ6A as a template from the Protein Data Bank. Additionally, the vaccine structure was modeled using Modeller to further improve its quality, as shown in Fig. .Afterwards, the modelled structure of the vaccine was refined and validated using the Ramachandran plot and z-score plot. The Ramachandran plot revealed that 93.5% of the amino acids in the vaccine protein were in the most preferred region, while 6.3% were in additional approved regions, 0.2% in the generously permitted regions, and no residues in the disallowed regions. The z-score of the modelled vaccine was − 5.08. Overall, the protein validation analyses revealed a reasonably consistent structure in the refined form, as shown in Fig. The ElliPro server was used to predict the conformational B-cell epitopes of the vaccine protein. The analysis revealed three non-contiguous B-cell epitopes, encompassing a total of 333 amino acid residues with values ranging from 0.506 to 0.675. The conformational epitopes varied in size, ranging from 4 to 394 residues. The three-dimensional representation of the conformational B-cell epitopes of the designed multi-epitope-based RSV vaccine and the epitope residues are presented in Fig. In the post-translational modification analysis of the vaccine, Four N-glycosylation sites and sixty-three O-glycosylation sites were predicted in the vaccine construct sequence. The findings suggest that a significant amount of glycosylation may have occurred inside the predicted vaccine construct, which might improve the vaccine's efficacy and immunogenicity. In addition, the vaccine protein sequence has ninety-six phosphorylated residues , threonine (T), and tyrosine (Y) phosphorylation sites) according to the NetPhos v2.0 server output. The server-provided plots containing the N-glycosylation sites and phosphorylation are given in Protein–protein docking analysis was performed to demonstrate the vaccine's ability to interact with various crucial molecular immune components i.e., TLRs. When docked using ClusPro 2.0, it demonstrated very high binding affinities with all its targets (TLRs). It has been further studied using the ZDOCK server where the vaccine protein also displayed very strong interaction with the TLRs. The lowest energy level obtained for docking between the vaccine construct and TLR-1, TLR-2, TLR-3, TLR-4, and TLR-9 were − 986.1, − 1236.7, − 1084.4, − 1260.8, and − 1226.4, respectively. The lowest energy level between the vaccine and TLRs indicated the highest binding affinity.13. Gromacs 2020.4 was used to carry out the production phase MD and the analysis of resulting trajectories was undertaken to understand the structural properties, and interaction between different TLRs and the predicted vaccine protein at a molecular level. The snapshots of equilibrated structures of each TLR-vaccine complex and the snapshots of the last trajectories are shown in Fig. MD simulation is an effective method for the analysis of biological systems and it provides many mechanistic insights into the possible behavior of the system under a simulated biological environment14. The results of total Rg are shown in Fig. 15. The results of the hydrogen bond analysis are shown in Root mean square deviations (RMSD) analysis gives insights into how the backbone atoms move relative to the initial equilibrated positions. Lower the RMSD, better the stability of the corresponding system. In the present work, we measured the RMSD in backbone atoms of entire protein–protein complexes of TLRs with a vaccine. Figure The immune simulation study of the designed vaccine was conducted using the C-ImmSimm server which forecasts the activation of adaptive immunity as well as the immune interactions of the epitopes with their specific targets. The analysis exhibited that the primary immune reaction to the vaccine could be stimulated substantially after administration of the vaccine, as demonstrated by a steady rise in the levels of different immunoglobulins i.e.,  Fig. A. It wasThe protein sequence of the vaccine was optimized for in-silico cloning and plasmid construction using the JCat server. The Codon Adaptation Index (CAI) value was 0.98, and the GC content was 50.23%, which falls within the desired range. The sequence was graphically illustrated after codon adaptation, as shown in 16, were analyzed in our study. To identify potential epitopes that can elicit an immune response in individuals with diverse human leukocyte antigen (HLA) genotypes, epitope prediction for multiple alleles was considered. The high polymorphism and variability in HLA allele combinations among individuals can significantly impact their ability to present and respond to specific epitopes, emphasizing the need for epitope prediction for multiple HLA alleles. This approach is essential in developing effective vaccines and immunotherapies that can target a wider population. A rigorous screening process was conducted to identify epitopes that were highly conserved, antigenic, non-allergenic, and non-toxic. This screening process was critical in ensuring that the vaccine is safe for use and has broad-spectrum activity against both RSV-A and RSV-B viruses. Specifically, HTL epitopes that demonstrated the ability to elicit cytokine production such as IFN-γ, IL-4, or IL-10 were ultimately chosen for the development of the vaccine. These cytokines are known to play a crucial role in modulating the immune response to viral infections such as RSV17. IFN-γ is a pro-inflammatory cytokine that has been strongly linked to respiratory syncytial virus (RSV) pathogenesis. Studies suggest that, IFN-γ plays a dual role in the control of RSV infection by limiting viral replication and preventing airway obstruction18. In contrast, IL-10 is an anti-inflammatory cytokine that has been demonstrated to regulate the immune response to viral infection by reducing the production of pro-inflammatory cytokines and limiting tissue damage caused by excessive inflammation20. IL-4 is also an anti-inflammatory cytokine that can play a crucial role in the regulation of the immune response to RSV infection by promoting Th2 cell differentiation and antibody production. It stimulates the production of immunoglobulin E (IgE) and Th2 cytokines, such as IL-5 and IL-13, which can help in clearing the virus and preventing further infection21. Therefore, cytokines like IFN-γ, IL-10, and IL-4 were given special consideration during the selection of HTL epitopes for the vaccine, since they may play a crucial role in mediating communication between various immune cells after the vaccine is administered22. In the MHC cluster analysis, the result presented indicates that the MHC Class-I and Class-II alleles and their epitopes, when considered separately, have a population coverage of over 85% globally, which suggests that the vaccine has the potential to provide protection to a large proportion of the world's population. When the MHC Class-I and Class-II alleles and their epitopes are combined, the coverage drops slightly to 84%, still representing a significant proportion of the population. Moreover, when the selected epitopes are compared to the overall population, they exhibit a greater individual percentage coverage, which suggests that the vaccine has the potential to be effective worldwide against RSV infections. This is an important consideration when developing vaccines since diseases like RSV can affect people globally, and a vaccine that can provide broad protection across different populations can be very valuable in preventing and controlling the spread of the disease. For vaccine construction, we selected four linkers  for conjugation with the most promising RSV epitopes based on their ability to provide optimal spacing, flexibility, and stability to the conjugates. The EAAAK linker contains glutamic acid and lysine residues, AAY contains alanine and tyrosine residues, GPGPG contains glycine and proline residues, and KK contains lysine residues. These linkers were chosen to enhance solubility, flexibility, stability, and immunogenicity of the conjugates25. An innate antimicrobial peptide, hBD-3, was selected as an adjuvant to improve the antigenicity, immunogenicity, durability, and longevity of the vaccine26. The peptide was chosen due to its potential to induce the expression of co-stimulatory molecules on the surface of monocytes and myeloid dendritic cells, as well as its ability to prevent the fusion of the virus and stimulate various immune responses28. Additionally, the PADRE sequence was incorporated to further strengthen the immunogenic reaction of the vaccine29. After constructing the vaccine, its antigenicity, allergenicity, and biophysical properties were assessed, revealing the vaccine protein to be suitable for further refinement and validation processes. The results indicate that the constructed vaccine protein is a basic, hydrophilic, and stable protein with desirable biophysical properties. The high theoretical pI, negative GRAVY value, low instability index, high extinction coefficient, and aliphatic index suggest that the protein is soluble, less likely to undergo conformational changes or degradation, and highly stable. These properties make the vaccine protein a promising candidate for further modelling refinement and validation33. The secondary structure analysis of the vaccine showed a higher abundance of coiled regions and a lower amount of β-strand, indicating greater stability and conservation of the predicted vaccine model. The tertiary structure was predicted and refined, resulting in a high p-value of 8.71e−05 in four domains of the protein, indicating an accurate prediction. In an effort to increase the stability of the vaccine structure, disulfide engineering was conducted to identify potential disulfides that can increase the protein's thermal stability34. Following the prediction and refinement of the tertiary structure of the vaccine, an analysis of disulfide engineering and posttranslational modifications was conducted. The aim of this analysis is to provide insight for future synthesis of the vaccine, with the potential to increase the vaccine's stability and immunogenicity. Through disulfide engineering, three pairs of amino acids in the vaccine were identified which could improve protein stability and facilitate the study of protein dynamics and interactions35. Further, the vaccine construct was analysed for posttranslational modifications and identified four N-glycosylation sites, sixty-three O-glycosylation sites, and ninety-six phosphorylated residues. Incorporating these modifications into vaccine designs can optimize vaccine efficacy and improve protection against viral infection. Posttranslational modifications, including glycosylation, phosphorylation, and acetylation, play a significant role in vaccine effectiveness and immunological responses. Glycosylation, in particular, can enhance antigenicity and immunogenicity, while phosphorylation and acetylation can promote immune recognition and improve protein stability38. The proposed RSV vaccine was analyzed using molecular docking, which revealed a strong interaction with Toll-like receptors (TLRs) critical in RSV pathogenesis. TLR4 showed the lowest energy level with the vaccine, indicating the highest interaction, while TLR2 and TLR9 also demonstrated high interactions. TLR2, TLR1, TLR6, TLR3, and TLR4 are critical TLRs in RSV pathogenesis, with TLR2/1 and TLR2/6 complexes enhancing early innate inflammatory responses and controlling viral replication. The vaccine's strong affinity with all target TLRs suggests that it may induce a robust immune response upon administration43. Following the molecular docking analysis, MD simulation was performed to examine the molecular stability of five docked vaccine-TLR complexes in response to external forces exerted by the surrounding environment. The TLR1-vaccine complex was found to be the most stable, while the TLR4-vaccine complex had higher RMSD despite having a similar number of chains. The RMSF analysis revealed that the residues in the range 190–240 and 480–530 had large magnitudes of fluctuations in all the complexes, with TLR3-vaccine complex showing slightly larger fluctuating side chains than others. The non-bonded interaction analysis showed that the TLR1-vaccine complex had the highest number of hydrogen bonds, with around 10 being formed till around 50 ns MD time interval, which steadily lowered to around 5 hydrogen bonds till 80 ns and thereafter rose to 10 hydrogen bonds. The TLR4-vaccine complex had the lowest number of hydrogen bonds formed, around 5, compared to other systems. Overall, the study showed that the docked vaccine-TLR complexes had reasonable stability, and the TLR1-vaccine complex had the highest stability among the five complexes. Actual residues involved in the hydrogen bond formation as investigated in the last trajectory are tabulated in the 44. The optimized vaccine DNA sequence was inserted into the pETite plasmid vector, containing SUMO and 6X His tags that could promote purification and downstream processing of the vaccine45. The stability of the vaccine mRNA secondary structure was predicted using Mfold and RNAfold servers, showing a much lower minimal free energies, indicating that the predicted vaccine could be very stable upon transcription. Overall, these findings suggest that the proposed vaccine construct has the potential for mass production and could be very stable, making it a promising candidate for further development. However, further research through in vitro or in vivo studies is required to strongly verify the immunogenicity, efficacy, stability, safety, and diverse biophysical properties of the proposed vaccine.Our study used immunoinformatics techniques to design a polyvalent epitope-based vaccine against RSV A and B subtypes by targeting four antigenic proteins of the viruses. The proteins' antigenicity and biophysical properties were predicted and found to be antigenic, with high aliphatic index, extinction coefficient, and negative GRAVY values, suggesting their stability, light-absorption ability, and hydrophilic nature. The potential T-cell and B-cell epitopes of the selected proteins, crucial in activating cytotoxic T-cells, helper T-cells, and B-cells to generate an effective immune responseThe high throughput immunoinformatics and MD approaches of vaccine designing are illustrated in a step-by-step processes in Fig. https://www.ncbi.nlm.nih.gov/) database, the RSV-A and RSV-B viruses were identified and selected along with their target proteins . The sequences of target proteins of the selected strains  were then extracted from the UniProt (https://www.uniprot.org/) database in FASTA format. The NCBI Protein database is a collection of SwissProt, PIR, PRF, and PDB sequences. It also includes GenBank, RefSeq, and TPA translations from elucidated coding regions.Through existing literature reviews in the National Center for Biotechnology Information (NCBI) (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html), the antigenicity of the target protein sequences was predicted with the prediction precision parameter threshold kept at 0.446. Subsequently, ProtParam tool of the ExPASy server (https://web.expasy.org/protparam/) was used to determine the biophysical properties of the target proteins47. Afterwards, the T-cell and B-cell epitope prediction was performed using the Immune Epitope Database or IEDB (https://www.iedb.org/), which contains extensive experimental data on antibodies and epitopes48. For the prediction of CTL epitopes for several HLA alleles, i.e., HLA A*01:01, HLA A*03:01, HLA A*11-01, HLA A*02:01, HLA A*02:06, and HLA A*29:02, the recommended IEDB NetMHCpan 4.0 prediction method was used. The default prediction method selection of the server is ‘IEDB recommended’ which utilizes the best available technique for a specific MHC molecule based on the availability of predictors and observes the predicted performance for a specific allele. Again, for the prediction of HTL epitopes for DRB1*03:01, DRB1*04:01, DRB1*15:01, DRB3*01:01, DRB5*01:01, and DRB4*01:01 alleles, the recommended IEDB 2.22 prediction method was used. Henceforth, based on their ranking, the top-scored HTL and CTL epitopes that were found to be common for all of the selected corresponding HLA alleles were considered for further analyses. All the parameters were retained by opting for default during the T-cell epitope prediction. Later, B-cell epitopes of the proteins were predicted using the BepiPred linear epitope prediction method 2.0, maintaining all the default parameters and thus, the top-scored LBL epitopes containing more than ten amino acids were primarily regarded as potential candidates for further analysis49. Also, the conformational B-cell epitopes of the modeled 3D structure of the vaccine were predicted using IEDB ElliPro, an online server (http://tools.iedb.org/ellipro/) using the default parameters of a minimum score of 0.5 and a maximum distance of 6 angstroms50.Before epitopes prediction, antigenicity and biophysical properties of the target proteins were analysed. Using the online antigenicity prediction tool, VaxiJen v2.0 (https://www.iedb.org/conservancy/) was used51. Also, the antigenicity determination tool VaxiJen v2.0 was used again for determination of antigenicity of the epitopes. Two different tools were then used, i.e. AllerTOP v2.0 (https://www.ddg-pharmfac.net/AllerTOP/) and AllergenFP v1.0 (http://ddg-pharmfac.net/AllergenFP/) to obtain the highest precision for prediction of allergenicity. Both of the tools are based on auto cross-covariance (ACC) transformation of protein sequences into uniform equal-length vectors. However, the AllerTOP v2.0 server has a better 88.7% prediction accuracy than the AllergenFP v1.0 server (87.9%)53. In addition, the ToxinPred (http://crdd.osdd.net/raghava/toxinpred/) server was used to predict toxicity for all epitopes by using the Support Vector Machine (SVM) prediction method to keep all the default parameters. Finally, using the TMHMM v2.0 server , the transmembrane topology prediction of all the epitopes was performed to predict whether the epitopes were exposed inside or outside, keeping the parameters at their default values. TMHMM uses an algorithm called N-best (or 1-best in this case) to predict the most probable location and orientation of transmembrane helices in the sequence54.In this step, several methods for predicting their conservancy, antigenicity, allergenicity, and toxicity were used to evaluate the initially predicted T-cell and B-cell epitopes. To assess the conservancy of the chosen epitopes, the conservancy prediction method of the IEDB server (http://crdd.osdd.net/raghava/ifnepitope/) server55. Based on analyzing a dataset that includes IFN-γ inducing and non-inducing peptides, the server determines the probable IFN-γ inducing epitopes. To determine the IFN-γ inducing capacity, the Design module and the Hybrid (Motif + SVM) prediction approach were used. In addition, IL-4 and IL-10 inducing HTL epitope properties were determined using the servers IL4pred (https://webs.iiitd.edu.in/raghava/il4pred/index.php) and IL10pred (http://crdd.osdd.net/raghava/IL-10pred/)57. The SVM method was used on both servers, where the default threshold values were kept at 0.2 and − 0.3, respectively.The induction capacity of the predicted HTL epitopes for interferon-γ (IFN-γ) was determined using the IFNepitope  protein module of the BLAST tool (https://blast.ncbi.nlm.nih.gov/Blast.cgi), where Homo sapiens (taxid: 9606) was used for comparison, keeping all other default parameters. An e-value cut-off of 0.05 was set and epitopes were selected as non-homologous pathogen peptides that showed no hits below the e-value inclusion threshold58. The epitopes found to be highly antigenic, non-allergenic, non-toxic, fully conserved, and non-homologous to the human proteome were finally considered as the most promising epitopes, and only these selected epitopes were used in the construction of the vaccine.The conservancy analysis of the specified epitopes was conducted using the IEDB server’s epitope conservancy analysis modulehttp://tools.iedb.org/population/) was used for analyzing the population coverage of the most promising epitopes across several HLA alleles in various regions around the world59. Denominated MHC restriction of T cell responses and polymorphic HLA combinations were considered in the analysis. All the parameters were maintained at their default conditions during the study. Furthermore, to evaluate the relationship between the selected MHC alleles, cluster analysis of the MHC alleles was done using the online tool MHCcluster 2.0 https://services.healthtech.dtu.dk/service.php?MHCcluster-2.0)60. During the study, 50,000 peptides to be used were retained, 100 bootstrap measurements were retained, and both HLA supertype (MHC Class-I) and HLA-DR (MHC class-II) representatives were chosen.The IEDB server (http://scratch.proteomics.ics.uci.edu/), holding all the default parameters61, to achieve better prediction precision. Three different online methods were used to accurately perform the allergenicity of the vaccine i.e. AlgPred , AllerTop v2.0 and AllergenFP v1.0, to ensure optimum prediction precision. The biophysical properties of the vaccine were then analysed ProtParam. Thereafter, the solubility of vaccine constructs was also estimated alongside the biophysical property study by the SOLpro module of the SCRATCH protein predictor (http://scratch.proteomics.ics.uci.edu/) and further validated by the Protein-Sol server (https://protein-sol.manchester.ac.uk/)62. All the parameters of the servers were maintained at their default values during the solubility review.The most promising antigenic epitopes were linked with each other to create a fusion peptide using an adjuvant and linkers. Human beta-defensin-3 (hBD-3) was used as an adjuvant sequence that was linked to the epitopes by EAAAK linkers. The epitopes were also appended to the pan HLA-DR epitope (PADRE) sequence. In the conjugation of the CTL, HTL, and LBL epitopes, the AAY, GPGPG, and KK linkers were used, respectively. Once the vaccine construct was prepared, antigenicity of the sequences was measured using VaxiJen v2.0 server, keeping the threshold value fixed at 0.4. The findings of the Vaxijen v2.0 server were further cross-checked by the ANTIGENpro module of the SCRATCH protein predictor (http://bioinf.cs.ucl.uk/psipred/) (using the PSIPRED 4.0 prediction method), GOR IV (https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_gor4.html), SOPMA (https://npsa-pbil.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html) and SIMPA96 (https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_simpa96.html). To predict the percentages or quantities of amino acids in α helix, β-sheet, and coil structure formations, these servers are considered to be reliable, quick, and effective67. Moreover, 3D modelling of the vaccine construct was carried out using the RaptorX online server (https://bio.tools/raptorx). Using an easy and powerful template-based method68, the server predicts the tertiary structure of a query protein. RaptorX uses a deep learning method to enable distance-based protein folding69.The vaccine construct was subjected to secondary structure prediction following biophysical property analysis. For this, several online tools were used to preserve all the default parameters, i.e. PSIPRED  using the GalaxyRefine module was used to refine the predicted model of the vaccine improving its resolution so that it resembles the native protein structure. The server uses dynamic simulation and the refinement approach is tested by CASP10 to refine the tertiary protein structures71. Furthermore, validation of the refined protein was carried out by analyzing the Ramachandran plot created by the PROCHECK (https://www.ebi.ac.uk/thornton-srv/software/PROCHECK/) tool72. Along with PROCHECK for protein validation, another online platform, ProSA-web (https://prosa.services.came.sbg.ac.at/prosa.php) was also used73. A z-score that expresses the consistency of a query protein structure is created by the PROCHECK server.GalaxyWEB server (http://cptweb.cpt.wayne.edu/DbD2/) online tool was used to predict the locations and further design the disulfide bonds within the vaccine proteins74. The tool was developed using computational approaches to predict the protein structure, and the algorithm of this server accurately estimates the χ3 torsion angle based on 5the Cβ–Cβ distance using a geometric model derived from native disulfide bonds. The Caf-Cβ-Sγ angle is allowed some tolerance in the DbD2 server based on the wide range found in native disulfides. To facilitate the ranking process, DbD2 estimates an energy value for each potential disulfide and mutant PDB files may be generated for selected disulfides76. The χ3 angle was held at − 87° or + 97° ± 10 during the experiment to cast off various putative disulfides that were generated using the default angles of + 97° ± 30° and − 87° ± 30°. Additionally, the angle of Caf-Cβ-Sγ was set to its default value of 114.6° ± 10. Finally, to allow disulfide bridge formation, residue pairs with energy less than 2.2 kcal/mol were selected and mutated to cysteine residue77.In this experiment, the Disulfide by Design (DbD) 2 v12.2 (http://www.cbs.dtu.dk/services/NetNGlyc-1.0), NetOGlyc4.0 (http://www.cbs.dtu.dk/services/NetOGlyc-4.0), and NetPhos-3.1 (http://www.cbs.dtu.dk/services/NetPhos-3.1) servers were utilized. The NetNglyc server uses artificial neural networks to predict N-glycosylation sites in human proteins by examining the sequence context of Asn-Xaa-Ser/Thr sequons78. The NetOglyc server predicts mucin type GalNAc O-glycosylation sites in mammalian proteins using neural networks79. This server provides a list of probable glycosylation sites for each input sequence, together with their positions in the sequence and prediction confidence scores. Using ensembles of neural networks, the NetPhos 3.1 server predicts serine, threonine, or tyrosine phosphorylation sites in eukaryotic proteins80.For posttranslational modification analysis of the vaccine construct comprising of the B-cell and T-cell epitopes, the NetNGlyc-1.0 (https://cluspro.bu.edu/login.php) was used to conduct the docking, where the lower energy score corresponds to the stronger binding affinity. Based on the following equation, the ClusPro server calculates the energy score:The vaccine protein was docked against several toll-like receptors (TLRs) in protein–protein docking analysis. In this study, different TLRs have been docked with the vaccine protein, i.e. TLR-1 (PDB ID: 6NIH), TLR-2 (PDB ID: 3A7C), TLR-3 (PDB ID: 2A0Z), TLR-4 (PDB ID: 4G8A), and TLR9 (PDB ID: 3WPF). ClusPro v2.0  method82. Furthermore, another round of docking was carried out using the ZDOCK server (https://zdock.umassmed.edu/) which is a rigid-body protein–protein docking tool that employs a combination of shape complementarity, electrostatics, and statistical potential terms for scoring and uses the Fast Fourier Transform algorithm to enable an efficient global docking search on a 3D grid. In the most current benchmark version (Accelerating protein docking in ZDOCK utilizing an advanced 3D convolution library), ZDOCK achieves high predictive accuracy on protein–protein docking benchmarks, with > 70% success in the top 1000 predictions for rigid-body instances83.The repulsions and attraction energies owing to van der Waals interactions are denoted by E84 MD simulation package. The CHARMM-36 force field parameters86 were employed to prepare the topology of protein chains. The system of each TLR along with the bound vaccine was solvated with the single point charge water model87 in the dodecahedron unit cells and neutralized with the addition of Na+ or Cl− counter-ions. The solvated systems were initially energy minimized to relieve the steric clashes if any with the steepest descent criteria until the threshold (Fmax < 10 kJ/mol) was reached. These energy minimized systems were then equilibrated at constant volume and temperature conditions 300 K using modified Berendsen thermostat88 and then at constant volume and pressure Berendsen barostat89 for 100 ps each. The equilibrated systems were later subjected to 100 ns production phase MD simulations, where the modified Berendsen thermostat and Parrinello-Rahman barostat90 were used with covalent bonds restrained using the LINCS algorithm91. The long-range electrostatic interaction energies were measured with the cut-off of 12 Å, with the Particle Mesh Ewald method (PME)92. The resulting trajectories were analyzed for root mean square deviations (RMSD) in protein backbone atoms, root mean square fluctuations (RMSF) in the side chain atoms of individual chains in each protein complex, the radius of gyration (Rg), and several hydrogen bonds formed between vaccine protein chain and the respective TLR protein chain.The docked complexes from the ZDOCK server were used in MD simulations. The complexes being protein–protein in nature with multiple chains, the MD simulations were computationally expensive and performed on the HPC cluster at Bioinformatics Resources and Applications Facility (BRAF), C-DAC, Pune with Gromacs 2020.4http://150.146.2.1/CIMMSIM/index.php) was used to predict real-life immune interactions using machine learning techniques and PSSM (Position-Specific Scoring Matrix)93. During the experiment, all the variables except for the time steps were kept at their default parameters. However, the time steps at 1, 84, and 170 were retained (time step 1 is injection at time = 0), and the number of simulation steps was set to 1050. Thus, three injections at four-week intervals were administered to induce recurrent antigen exposure94.To forecast the immunogenicity and immune response profile of the proposed vaccine, an immune simulation analysis was performed. For the immune simulation study, the C-ImmSim server (http://www.jcat.de/) was used for codon optimization95, and the optimized codon sequence was further analyzed for expression parameters, codon adaptation index (CAI), and GC-content %. The optimum CAI value is 1.0, while a score of > 0.8 is considered acceptable, and the optimum GC content ranges from 30 to 70%96. For in silico cloning simulation, the pETite vector plasmid was selected which contains a small ubiquitin-like modifier (SUMO) tag as well as a 6 × polyhistidine (6X-His)  tag97. The vaccine protein sequence was reverse-translated to the optimized DNA sequence by the server to which EaeI and StyI restriction sites were incorporated at the N-terminal and C-terminal sites, respectively. The newly adapted DNA sequence was then inserted between the EaeI and StyI restriction sites of the pETite vector using the SnapGene restriction cloning software  to confirm the expression of the vaccine99.Java Codon Adaptation Tool or JCat server  and RNAfold (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi), were used for the mRNA secondary structure prediction101. Both of these servers thermodynamically predict the mRNA secondary structures and provide each of the generated structures with minimum free energy . To analyze the mRNA folding and secondary vaccine structure, the optimized DNA sequence was first taken from the JCat server and converted via the DNA <-> RNA-> Protein tool  to a possible RNA sequence. The RNA sequence was then gathered from the tool and utilized for prediction into the Mfold and RNAfold servers using the default settings for all the parameters.Two servers, i.e. Mfold (Supplementary Information.